Probabilistic Mining in Large Transaction Databases

Abstract

In this era of big data analysis, mining results hold a very important role. So, the data scientists need to be accurate enough with the tools, methods and procedures while performing rule mining. The major issues faced by these scientists are incremental mining and the huge amount of time that is virtually required to finish the mining task. In this context, we propose a new rule mining algorithm which mines the database in a probabilistic approach for finding interesting relations. This paper also compares the new technique with the traditional Apriori, FP Growth and Eclat algorithms. The proposal has also been tested against the various modified approaches of these algorithms. The proposed algorithm finishes the task in O (n) in its best case analysis and in O (n log n) in its worst case analysis. The algorithm also considers less frequent high priority attributes for rule creation, thus makes sure the creation of valid mining rules. The major issue of traditional algorithms was the generation of invalid rules, longer running time and high memory utilizations. This could be remedied by this new proposal. The algorithm was tested against various datasets and the results were evaluated and compared with the traditional algorithm. The results showed a peak performance improvement.